Latest AI and machine learning research in oncology/hematology for healthcare professionals.
This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from stud...
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and...
PURPOSE: Colorectal tumor segmentation is an important step in the analysis and diagnosis of colorectal cancer. This task is a time consuming one sinc...
Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased ...
BACKGROUND AND OBJECTIVES: Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategie...
PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation therapy (IGRT) of prostate cancer patients because int...
BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patie...
The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodule...
PURPOSE: The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging moda...
Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We appl...
PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant ...
Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of tradi...
Background Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging fea...
Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body...
BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatical...
OBJECTIVE: To clarify CT diagnostic performance in extranodal extension of cervical lymph node metastases using deep learning classification.
Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for ...
PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being....
PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In thi...
Cell isolation by eliminating undesirable cell aggregations or colonies with low activity is essential to improve cell culture efficiency. Moreover, w...